



'''simple docstring'''

import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD

class __snake_case( nn.Module   ):


						'''simple docstring'''



						UpperCAmelCase				:       Union[str, Any] 	=						42
						UpperCAmelCase				:       Union[str, Any] 	=						42
						UpperCAmelCase				:       Optional[Any] 	=						0.0
						UpperCAmelCase				:       int 	=						1
						UpperCAmelCase				:       List[Any] 	=						1
						UpperCAmelCase				:       List[Any] 	=						True
						UpperCAmelCase				:       List[str] 	=						False
						UpperCAmelCase				:       Dict 	=						False
						UpperCAmelCase				:       Tuple 	=						False
						UpperCAmelCase				:       str 	=						jnp.floataa


						def 							__snake_case       (   self )      ->      Tuple:
							lowerCAmelCase       =      []
							lowerCAmelCase       =      []

							for i in range(self.num_layers ):
								lowerCAmelCase       =      self.in_channels if i == 0 else self.out_channels

								lowerCAmelCase       =      FlaxResnetBlockaD(
								    in_channels=UpperCamelCase__      ,			out_channels=self.out_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
								resnets.append(UpperCamelCase__ )

								lowerCAmelCase       =      FlaxTransformeraDModel(
								    in_channels=self.out_channels      ,			n_heads=self.num_attention_heads      ,			d_head=self.out_channels // self.num_attention_heads      ,			depth=1      ,			use_linear_projection=self.use_linear_projection      ,			only_cross_attention=self.only_cross_attention      ,			use_memory_efficient_attention=self.use_memory_efficient_attention      ,			dtype=self.dtype      ,			)
								attentions.append(UpperCamelCase__ )

							lowerCAmelCase       =      resnets
							lowerCAmelCase       =      attentions

							if self.add_downsample:
								lowerCAmelCase       =      FlaxDownsampleaD(self.out_channels      ,			dtype=self.dtype )




						def __call__(   self      ,			A_      ,			A_      ,			A_      ,			A_=True )      ->      Any:
							lowerCAmelCase       =      ()

							for resnet, attn in zip(self.resnets      ,			self.attentions ):
								lowerCAmelCase       =      resnet(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )
								lowerCAmelCase       =      attn(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )
								output_states += (hidden_states,)

							if self.add_downsample:
								lowerCAmelCase       =      self.downsamplers_a(UpperCamelCase__ )
								output_states += (hidden_states,)

							return hidden_states, output_states







class __snake_case( nn.Module   ):


						'''simple docstring'''



						UpperCAmelCase				:       Optional[int] 	=						42
						UpperCAmelCase				:       List[str] 	=						42
						UpperCAmelCase				:       List[Any] 	=						0.0
						UpperCAmelCase				:       Union[str, Any] 	=						1
						UpperCAmelCase				:       Any 	=						True
						UpperCAmelCase				:       Any 	=						jnp.floataa


						def 							__snake_case       (   self )      ->      Optional[Any]:
							lowerCAmelCase       =      []

							for i in range(self.num_layers ):
								lowerCAmelCase       =      self.in_channels if i == 0 else self.out_channels

								lowerCAmelCase       =      FlaxResnetBlockaD(
								    in_channels=UpperCamelCase__      ,			out_channels=self.out_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
								resnets.append(UpperCamelCase__ )
							lowerCAmelCase       =      resnets

							if self.add_downsample:
								lowerCAmelCase       =      FlaxDownsampleaD(self.out_channels      ,			dtype=self.dtype )




						def __call__(   self      ,			A_      ,			A_      ,			A_=True )      ->      Dict:
							lowerCAmelCase       =      ()

							for resnet in self.resnets:
								lowerCAmelCase       =      resnet(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )
								output_states += (hidden_states,)

							if self.add_downsample:
								lowerCAmelCase       =      self.downsamplers_a(UpperCamelCase__ )
								output_states += (hidden_states,)

							return hidden_states, output_states







class __snake_case( nn.Module   ):


						'''simple docstring'''



						UpperCAmelCase				:       Union[str, Any] 	=						42
						UpperCAmelCase				:       Tuple 	=						42
						UpperCAmelCase				:       Any 	=						42
						UpperCAmelCase				:       Dict 	=						0.0
						UpperCAmelCase				:       Optional[Any] 	=						1
						UpperCAmelCase				:       Optional[int] 	=						1
						UpperCAmelCase				:       Any 	=						True
						UpperCAmelCase				:       Any 	=						False
						UpperCAmelCase				:       Optional[int] 	=						False
						UpperCAmelCase				:       List[str] 	=						False
						UpperCAmelCase				:       List[Any] 	=						jnp.floataa


						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      []
							lowerCAmelCase       =      []

							for i in range(self.num_layers ):
								lowerCAmelCase       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
								lowerCAmelCase       =      self.prev_output_channel if i == 0 else self.out_channels

								lowerCAmelCase       =      FlaxResnetBlockaD(
								    in_channels=resnet_in_channels + res_skip_channels      ,			out_channels=self.out_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
								resnets.append(UpperCamelCase__ )

								lowerCAmelCase       =      FlaxTransformeraDModel(
								    in_channels=self.out_channels      ,			n_heads=self.num_attention_heads      ,			d_head=self.out_channels // self.num_attention_heads      ,			depth=1      ,			use_linear_projection=self.use_linear_projection      ,			only_cross_attention=self.only_cross_attention      ,			use_memory_efficient_attention=self.use_memory_efficient_attention      ,			dtype=self.dtype      ,			)
								attentions.append(UpperCamelCase__ )

							lowerCAmelCase       =      resnets
							lowerCAmelCase       =      attentions

							if self.add_upsample:
								lowerCAmelCase       =      FlaxUpsampleaD(self.out_channels      ,			dtype=self.dtype )




						def __call__(   self      ,			A_      ,			A_      ,			A_      ,			A_      ,			A_=True )      ->      Union[str, Any]:






							for resnet, attn in zip(self.resnets      ,			self.attentions ):
								# pop res hidden states
								lowerCAmelCase       =      res_hidden_states_tuple[-1]
								lowerCAmelCase       =      res_hidden_states_tuple[:-1]
								lowerCAmelCase       =      jnp.concatenate((hidden_states, res_hidden_states)      ,			axis=-1 )

								lowerCAmelCase       =      resnet(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )
								lowerCAmelCase       =      attn(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )

							if self.add_upsample:
								lowerCAmelCase       =      self.upsamplers_a(UpperCamelCase__ )

							return hidden_states







class __snake_case( nn.Module   ):


						'''simple docstring'''



						UpperCAmelCase				:       Any 	=						42
						UpperCAmelCase				:       Optional[Any] 	=						42
						UpperCAmelCase				:       List[Any] 	=						42
						UpperCAmelCase				:       List[Any] 	=						0.0
						UpperCAmelCase				:       List[Any] 	=						1
						UpperCAmelCase				:       Optional[int] 	=						True
						UpperCAmelCase				:       List[str] 	=						jnp.floataa


						def 							__snake_case       (   self )      ->      str:
							lowerCAmelCase       =      []

							for i in range(self.num_layers ):
								lowerCAmelCase       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
								lowerCAmelCase       =      self.prev_output_channel if i == 0 else self.out_channels

								lowerCAmelCase       =      FlaxResnetBlockaD(
								    in_channels=resnet_in_channels + res_skip_channels      ,			out_channels=self.out_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
								resnets.append(UpperCamelCase__ )

							lowerCAmelCase       =      resnets

							if self.add_upsample:
								lowerCAmelCase       =      FlaxUpsampleaD(self.out_channels      ,			dtype=self.dtype )




						def __call__(   self      ,			A_      ,			A_      ,			A_      ,			A_=True )      ->      Dict:






							for resnet in self.resnets:
								# pop res hidden states
								lowerCAmelCase       =      res_hidden_states_tuple[-1]
								lowerCAmelCase       =      res_hidden_states_tuple[:-1]
								lowerCAmelCase       =      jnp.concatenate((hidden_states, res_hidden_states)      ,			axis=-1 )

								lowerCAmelCase       =      resnet(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )

							if self.add_upsample:
								lowerCAmelCase       =      self.upsamplers_a(UpperCamelCase__ )

							return hidden_states







class __snake_case( nn.Module   ):


						'''simple docstring'''



						UpperCAmelCase				:       Dict 	=						42
						UpperCAmelCase				:       Any 	=						0.0
						UpperCAmelCase				:       Any 	=						1
						UpperCAmelCase				:       Dict 	=						1
						UpperCAmelCase				:       Union[str, Any] 	=						False
						UpperCAmelCase				:       Dict 	=						False
						UpperCAmelCase				:       str 	=						jnp.floataa


						def 							__snake_case       (   self )      ->      Optional[Any]:
							lowerCAmelCase       =      [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels      ,			out_channels=self.in_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
							]

							lowerCAmelCase       =      []

							for _ in range(self.num_layers ):
								lowerCAmelCase       =      FlaxTransformeraDModel(
								    in_channels=self.in_channels      ,			n_heads=self.num_attention_heads      ,			d_head=self.in_channels // self.num_attention_heads      ,			depth=1      ,			use_linear_projection=self.use_linear_projection      ,			use_memory_efficient_attention=self.use_memory_efficient_attention      ,			dtype=self.dtype      ,			)
								attentions.append(UpperCamelCase__ )

								lowerCAmelCase       =      FlaxResnetBlockaD(
								    in_channels=self.in_channels      ,			out_channels=self.in_channels      ,			dropout_prob=self.dropout      ,			dtype=self.dtype      ,			)
								resnets.append(UpperCamelCase__ )

							lowerCAmelCase       =      resnets
							lowerCAmelCase       =      attentions




						def __call__(   self      ,			A_      ,			A_      ,			A_      ,			A_=True )      ->      str:
							lowerCAmelCase       =      self.resnets[0](UpperCamelCase__      ,			UpperCamelCase__ )
							for attn, resnet in zip(self.attentions      ,			self.resnets[1:] ):
								lowerCAmelCase       =      attn(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )
								lowerCAmelCase       =      resnet(UpperCamelCase__      ,			UpperCamelCase__      ,			deterministic=UpperCamelCase__ )

							return hidden_states

from __future__ import annotations

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __magic_name__	,       __magic_name__								=			array[indexa], array[indexa]

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    for i in range(A_,	low + middle   ):
      comp_and_swap(A_,	A_,	i + middle,	A_   )
    bitonic_merge(A_,	A_,	A_,	A_   )
    bitonic_merge(A_,	low + middle,	A_,	A_   )

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    bitonic_sort(A_,	A_,	A_,	1   )
    bitonic_sort(A_,	low + middle,	A_,	0   )
    bitonic_merge(A_,	A_,	A_,	A_   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	List[Any]       			=  [int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')

